Financialization involves a highly disciplined neoliberal landscape where state power structures and private technologies facilitate and protect the activities and interests of finance capitalism over all else. Within this insulated environment, financialization occurs via securitization, which simply described, is a process where financial institutions bundle together (illiquid) financial assets – primarily loans – and transform them into (liquid) tradable securities that can be expeditiously bought and sold in secondary financial markets. Within this globalized environment, digital securities trading – including “fictitious” trading, hedging and speculating in derivative markets – generates “phantom wealth”; whereby the exchange of capital, money and currency is detached from material or labor value. In the twenty-first century, debt is the new global currency and is a primary source of (intangible) wealth accumulation.

Rebooting the System for a New Age

Writing in Forbes magazine in 2013, technology entrepreneur Naveen Jain made an assessment of the historical origins of mass public education by pointing out that, “Our education system was developed for an industrial era.” Jain went on to explain that the U.S. education system,

…today uses the mass production style manufacturing process of standardization. This process requires raw material that is grouped together based on a specific criteria. Those raw materials are then moved from one station to another station where an expert makes a small modification given the small amount of time given to complete their task. At the end of the assembly line, these assembled goods are standardized tested to see if they meet certain criteria before they are moved to the next advanced assembly line.

Jain makes this point not as a critique of education serving the interests of industrial capitalism through the application of the scientific management model of production (Taylorism) to schooling. On the contrary, he does so to make a case that current education reform policies are a continuation of the original mission of U.S. public education as an instrument of social efficiency, yet only being modernized to bolster financialized capitalism. As Jain puts it, “Our education system is not broken, it has just become obsolete.” He goes on to explain:

When I think of all the tremendous, seemingly impossible feats made possible by entrepreneurs, I am amazed that more has not been done to reinvent our education system. I want all entrepreneurs to take notice that this is a multi-hundred billion dollar opportunity that’s ripe for disruption.

The means by which such financial “opportunities” reside by “reinventing” education are made more explicit when Jain goes on to claim, “Rethinking education starts with embracing our individuality… just think of the opportunities we can unlock by making education as addictive as a video game” by flipping the current model on its head and use “technology to focus on our learners.” Using the same historical context that Jain does to support this argument, the superintendent of Miami-Dade County Public Schools (and rising star in the education reform industry), Alberto Carvalho decreed in 2015, “Unfortunately, for most American students the old factory model of education still applies. This is a recipe for failure and frustration. We cannot address Digital Age needs with Industrial Age education.” Carvalho goes on to claim:

We must leave behind us the days of sorting students by age and instruction by subject. More and more, our 8th-graders are studying alongside 6th-graders of similar ability, interests and readiness. After all, we aren’t grouped by age in the employment marketplace. No one told Mark Zuckerberg he couldn’t be CEO of Facebook because he wasn’t born the same year as Bill Gates.

Jain and Carvalho’s edicts are an integral part of the education technology (EdTech) industry’s marketing narrative, as a driving force and beneficiary of the financialization of public education. Be it venture philanthropists, federal and state policymakers or EdTech executives, the current mission of education reform is to “reinvent” education, propelled by a narrative of benevolent intent and remedied by meeting the needs of financial markets through embracing education technologies. In doing so, the EdTech industry promotes its products as being student-centered, competency-based “anytime-anywhere learning” or more specifically as “personalized learning.” According to its advocates, personalized learning simply means the differentiation of digitized coursework for students based on their different skill levels that allows them to engage in learning activities at their own pace through the use of digital tools. Accordingly, the Gates Foundation claims on its Personalized Learning page, “In personalized learning, the student is the leader, and the teacher is the activator and the advisor.” On its Digital Tools and Content page, the foundation goes on to report that personalized learning “technology is not just a way for students to pursue their interests; it is way for them to discover their interests.” Thus, personalized learning promises to revolutionize American education and positions EdTech to be the vanguard in liberating students to take control of their learning. As marketing and communications professional Jesse Irwin puts it,

Since 2011, billions of dollars of venture capital investment have poured into public education through private, for-profit technologies that promise to revolutionize education… these tools promise to remedy the many, many societal ills facing public education with… technological advancements.

Like a visionary leader of a social movement, Superintendent Carvalho calls us to action by proclaiming, “Now is the time for transformation, but we must do more than reboot the system; we must redesign it for the demands of a new age, reaching and teaching each student in the ways he learns best. It’s that simple, and that hard. All we need is the will, skill and belief to change.”

Ultimately, personalized learning entails immersing children in digitized software and is at the forefront of facilitating the disruption and replacement of traditional public schooling, yet in even more officious and imperious ways. To understand this better, we must ask ourselves: how is personalized learning personal? Contextualizing EdTech within the larger technological landscape is important to truly get to the root of the answer to this question; as well as how it fits into schooling as a function of social control within the 21st century cultural political economy. To answer this, I will first take a step back and widen the scope before I focus more deeply on this fundamental question.

From Pinkerton’s to Big Data

True to the lineage of industrial capitalism and neoliberal capitalism, financialization is inherently hostile to social movements that strive for social justice, reparations, redistribution and participatory parity. Within nation-states under this domain, public institutions, social programs, laws, policing and systems of “justice” are primarily designed to serve the interests of power and thus exist for the purpose of social control and domination. Authoritarian practices in the U.S. have long been employed to surveil and monitor active, perceived or potential resistance efforts. The U.S. government and the private sector have long partnered in these efforts within these dynamics. Yet, as Alvaro Bedoya from the Center on Privacy and Technology at Georgetown Law describes it in Slate Magazine, not everyone is watched equally:

[a]cross our history and to this day, people of color have been the disproportionate victims of unjust surveillance; [FBI Director] Hoover was no aberration. And while racism has played its ugly part, the justification for this monitoring was the same we hear today: national security.

Chattel slavery, Jim Crow, Native Reservations, laws governing Mexican Americans and the schooling of Black, Brown and Indigenous people have always served as massive structures of surveillance and control.

Class struggle and war resistance in the U.S. has also been at the forefront of surveillance. Beginning in the 1850s, business leaders in Chicago sponsored the establishment of the private Pinkerton National Detective Agency to infiltrate and spy on organized labor. In 1870, the U.S. Justice Department was established, often contracting with private detective firms, most notably Pinkerton detectives. In the early 20th century, the federal government extensively monitored and prosecuted leftist organizations and leaders. The “Great War” (World War I) was an incredibly unpopular war in the U.S., with resistance often being led by organized labor and leftist groups; leading to U.S. military intelligence agencies contracting with the Northern Information Bureau, a private union-busting security firm, to spy on the activities of the Communist Party and the International Workers of the World (IWW). As has been widely documented, throughout the 20th century, actual or suspected communists, socialists and anarchists were constantly monitored and surveilled by the U.S. government and private industry.

In his book Bitter Freedom, Maurice Walsh chronicles how after World War I many whites feared that returning Black veterans, “who had displayed nerves of steel” on the battlefield would return with a new found confidence that would lead them to fight for their own rights at home. White fear of Black liberation was also a reality as reflected by Black nationalist leader Marcus Garvey when he declared before twenty-five thousand Black Americans at a 1920 gathering in New York City: “We new negroes…we men who have returned from this war, will dispute every inch of the way until we win.” At once inciting anti-Blackness and the “Red Scare”, President Woodrow Wilson declared that these emboldened Black men would be “the greatest medium in conveying Bolshevism to America.” This was the era of Jim Crow and these fomented fears led to an even greater surge in anti-Black violence across the nation, often times targeting Black veterans who had survived the slaughter of trench warfare in France. Several of the 78 Black men lynched in 1919 were wearing their military uniforms. White fear of Black liberation during this period also led to the creation of a special Military Intelligence Division that focused on “Negro Subversion.” Between 1917-1941, this military unit’s mission was to surveil the activities of Black Americans in both civilian and military life and to report suspicious activities to other federal agencies, particularly the Department of Justice and Federal Bureau of Investigation. At the top of their list was W.E.B. DuBois.

During World War II, Japanese Americans were surveilled by using Census Data as a means to intern them as prisoners of war. Throughout the 1960s and 1970s, civil rights leaders, organizations and activists were regularly surveilled and monitored by the FBI. Martin Luther King was surveilled by the National Security Association (NSA) and the FBI. The FBI’s wiretapping of King – approved by Attorney General Robert Kennedy – was justified by attempts to tie King to the Communist Party. The labor and civil rights leader Cesar Chavez was monitored for years, justified by his having ties to communists. The American Indian Movement, The Chicano Movement and the Black Panther Party were all famously surveilled and infiltrated by the FBI’s COINTELPRO (COunter INTELligence PROgram) as a means to undermine their emancipatory activities and to prosecute their leaders.

As Bedoya chronicles, municipal and state police departments have a long history of engaging in racial profiling practices in multiple ways. New York City’s “Stop-and-Frisk” program is just one example of such policies and practices. Since 9/11, state and federal agencies have been targeting Muslims in the U.S., using a wide spectrum of surveillance strategies. Also, according to Bedoya, “the Department of Homeland Security—an agency founded to protect against terror attacks—has been tracking Black Lives Matter activists. If you name a prominent civil rights leader of the 20th or 21st centuries, chances are strong that he or she was surveilled in the name of national security.” The single largest domestic spying program in our nation’s history – the NSA’s call records program – was tested out by the Drug Enforcement Administration for nearly a decade on Latinx immigrants.

Big Data, Surveillance, and Financialization

The 21st century is an age where massive quantities of digital information (data) is being captured, stored, tracked, analyzed and bought and sold by private firms and government agencies. Enormous amounts of data are collected every minute of every day from online activities via computers, tablets, mobile devices, smart phone apps and smart machines. This includes web server logs and clickstream data (every click made), social media content and social network activity, shopping and credit card use, text from emails and survey responses, mobile-phone call records, and more. Mobile devices track travel patterns and driving speed. Everything that is or becomes digital is collected, and contributes to an ever accumulating behavioral data profile for everyone. This personal profile also includes medical, mental health, employment, education and government records, including the U.S. Census.

This mass accumulation of digital data is the basis for what is called “Big Data.” According to data systems expert Rohit Rai, “Big Data relates to data creation, storage, retrieval and analysis that is remarkable” in terms of volume (how much data), velocity (how fast data is processed), and variety (the various types of data). It was the symbiotic relationship among Google, Yahoo, Facebook, Twitter, LinkedIn, Amazon, Netflix and other large Internet companies that propelled Big Data early on, all of which were heavy users as well as creators of fundamental Big Data technologies. These are the companies that established industry standards in creating the “culture of analytics” that pervades every aspect of their business. Big data is a fundamental structure of the financialized economy that is propelled by the Internet, cloud computing, mobile devices and social media, intended to create generations of hyper-connected consumers.

Big Data begins with data collection, which feeds into the data mining pipeline, a process which encompasses three intertwined scientific disciplines: the numeric study of data relationships (statistics); human-like intelligence displayed by software and/or machines (artificial intelligence); and algorithms that can learn from data to make predictions (machine learning). According Skylads, a digital software company, Artificial Intelligence refers to computers, machines and systems that are capable of “natural language processing (i.e. communicate with no trouble on a given language); automated reasoning (using stored information to answer questions and draw new conclusions) and machine learning (the ability to adapt to new circumstances and detect patterns).”

Machine Learning has been fundamental in the development of artificial intelligence, enabling machines to learn and adapt when exposed to massive amounts of data. Historically, machine learning enabled a system to acquire knowledge, but only through human supervised learning experiences. Currently, machine learning is innovating into “Deep Learning” systems, which enables more general, powerful, and faster machine learning. Deep learning empowers machines with perceptual learning capabilities – unsupervised by humans – to react to real-world visual, auditory and natural language data; then responds in intelligent ways. According to the deep learning company Leverton, “Deep learning technology… is based on the idea of programming algorithms to imitate functions of neurons in the human brain.” Data analytics are essential to the advancement of machine learning and deep learning systems. Data analytics involves the confluence of four distinct types of analytics: Descriptive Analytics (what has happened or what is happening); Diagnostic Analytics (why did it happen); Predictive Analytics (what is likely to happen) and Prescriptive Analytics (what should happen to influence future outcomes). Descriptive analytics is the starting point and as more detailed and contextual data is gathered over time, this allows for more sophisticated deep learning algorithms to be applied and for the three subsequent types of analytics. Although these algorithms are invisible to us, Michael Evans of Dartmouth College explains that with analytics:

We see their output as recommendations about what we should do, or about what should be done to us. Netflix suggests your next TV show. Your car reminds you it’s time for an oil change. Siri tells you about a nearby restaurant. Machine-learning algorithms monitor information about what you do, find patterns in that data, and make informed guesses about what you want to do next. Without you, there’s no data, and there’s nothing for machine learning to learn.

According to deep learning scientist, Michael Wu, predictive analytics does not predict one potential future, but “multiple futures” centered on a decision-maker’s preferred actions. Wu contends that, “[s]ince a prescriptive model is able to predict the possible consequences based on different choice of action, it can also recommend the best course of action for any pre-specified outcome.”

Social media has always been a commercial venture and its primary purpose as a profit generator quickly became about data mining, particularly in terms of sentiment mining for predictive and prescriptive analysis. Sentiment analysis (opinion mining) is a subset of predictive analysis and determines if online expressions – text, “likes”, emoticons, etc. – are positive, negative or neutral as means to determine how people feel about specific topics. Sentiment analysis gathering software scans across all social media conversations like Facebook, Twitter blogs, news, forums, videos, reviews, images, etc., collecting data streams for analysis via deep learning algorithms that classify and derive meaning. According to Sandeep Raut, the Director for Digital Transformation at Syntel:

Nestle, via their Digital Acceleration Team, tracks the sentiments of their 2000+ brands to know what their customers think and to deliver products that they want and to prevent crisis’s from happening. Coca-Cola, the brand that built its marketing message around happiness and sharing, has built vending machines which sets the price of a can based on how positive your tweets are. Consumers are always on their smartphones leaving the trails of their feelings in the digital world.

There is an abundance of data across various vertical markets in banking, financial services, insurance, healthcare, life sciences, retail, consumer goods, manufacturing, travel and hospitality, IT, telecommunication, media, entertainment, government, and more. This boon is driving demand for the most current and innovative deep learning and analytics related products. The financialized global economy thrives on high speed information processing on many levels. Big Data has become the essential infrastructure of it. The three v’s (volume, velocity and variety) of Big Data mining is not enough to support investors and finance professionals in their activities of high frequency trading, fund management, exploitation of markets and management of risk exposure. Thus, the industry demands two additional v’s – veracity (accuracy) and value (market value) that comes with the innovations of AI’s deep learning systems, specifically predictive and sentiment analytics.

Working alongside data scientists, financial experts are automating the extraction of sentiment from a rapidly expanding array of sources to better understand the personalized reactions of individuals and groups (investors and consumers) to specific and real time information. Data attained from sources such as news wires, economic announcements, social media, micro blogs, twitter, online search engines, Wikipedia, etc., are invaluable instruments of this Business Intelligence (BI) apparatus. According to a publication put out by TCS’ Global Consulting titled: Tuning in to the Emotions of the Capital Markets with Sentiment Analysis, “real-time social data about customers’ family situation, business interests, passions, behavior patterns and decisions, along with data from other systems…provides a deeper understanding of customers.” The customer analytics company Buxton, goes on to explain how companies and financial firms that couple customer analytics and predictive analytics software to their data mining activities,

…can unlock who exactly your best customers are – looking at more than just demographics, but actually understanding what lifestyle characteristics your best customers have, including how they spend their money and live their lives. Once we understand the attributes of your best customers, we are able to show where everyone who looks just like those best customers lives – down to the household level – anywhere in your operating areas… More importantly, we’re able to tell you the value that each of those potential customers is worth…

Social media has become a primary data mining source for the retail industry (flush with private equity investors, while rapidly becoming an impact investment offering), due to its capacity to obtain instant product and service feedback via social networking sites and blogs.

Big Data is also integrating machine-generated data that is automatically captured (without human intervention) by sensors connected to the Internet of Things (IoT). According to Internet Society, the IoT’s describes:

…scenarios in which network connectivity and computing capability extends to a constellation of objects, devices, sensors, and everyday items that are not ordinarily considered to be “computers’’; this allows the devices to generate, exchange, and consume data, often with minimal human intervention.

As Eran Levy from the business analytics company Sisense reported in 2014, we live in a world where everything will soon be equipped with an IP address, “from your bicycle to your pens to your washing machine. All these things will be linked and reported. Most importantly, they will be generating tons of data… everything you do can be recorded and analyze.” According to the University of Phoenix Research Institute:

Every object, every interaction, everything we come into contact with will be converted into data. Once we decode the world around us and start seeing it through the lens of data, we will increasingly focus on manipulating the data to achieve desired outcomes. Thus we will usher in an era of “everything is programmable.

Basically, IoT means that everything everywhere is being technologized, connected to a vast network that feeds the Business Intelligence and state intelligence ecosystem that is Big Data. In essence quantitative data – largely our own personal data – will increasingly be used to “manipulate” us and “program” our environments according to the demands of powerful interests.

As the Chicago Tribune reported in 2016, a rapidly growing component of this vast ecosystem is the biometrics data market, which is projected to be worth $21.9 billion by 2020. As part of this data market, physiological biometrics involves technologies that labels and describes individuals and groups through physiological characteristics, largely for identification and authentication (access control) purposes. Physical identifiers include, but are not limited to, fingerprints, voice, face, ear, iris and retina recognition, DNA, vein patterns, palm prints, hand geometry and scent. Behavioral biometrics uses data gathering technology that builds a unique behavior profile on individual users of devices, based on keystroke and mouse movement analysis and voice and gait recognition (the way people walk). Writing in the financial services publication CFO in 2015, Neuburger claimed, “Biometrics is the practice of using a digital representation of a person’s individual’s physical characteristics as a means to identify that specific person ‘out of a crowd.’”

Additionally, biosensor enabled mobile, wearable, indigestible, implanted, tattooed and contact lens devices monitor, track, compile and transmit data about our overall health status, lifestyle and performance levels. This information can be remotely detected and monitored in real time and then integrated into the larger Big Data infrastructure. According to PSFK labs, “the world’s leading provider of innovation insights,” embedded sensory and display technologies will soon be commonplace, outwardly conveying “information about the wearer and his/her reaction to the surrounding environment. Responding to everything from an individual’s emotional state to their interactions with others with light, color and opacity, these adaptive materials create a novel communication stream that informs both the wearer and those around them.” Biosensor technology can also detect drug and alcohol use and stress/anxiety levels. When attached to analytic programs, biometric data is used for predictive purposes in terms of medical and mental health diagnosis and intervention. Biometrics is already being used to link human behavior and physiological data to workforce performance, a topic that requires an entire book to itself.

Video analytic technology is developing and increasingly being integrated into the Big Data and IoT ecosystem. Writing in Wired magazine, Sean Verah describes how sophisticated digital video recording devices using computer vision algorithms that automatically analyze video in real time and over time are currently being utilized in various ways by business and government. Very soon this technology will have the capacity to survey every location on the planet from land, sea, air and space; identifying hundreds of people (with gait, facial and other recognition abilities) and objects within any given scene, while tracking their movements and behavior. Writing in Information Week, Lisa Morgan claims, “the Internet of Things is gaining momentum” whereby “sensors are now small and cheap enough to embed in all kinds of devices, and more companies are leveraging the vast data generated.”

Data expert Phil Harvey tells us, “Consider your world. It is data now. Data is in everything we do. Especially in business.” Writing in the Harvard Business Review, Randy Bean reports how Big Data has become firmly established within Fortune 1000 firms, especially in the financial industry, “where data is plentiful and data investments are substantial.” The reliance on Big Data in the financial industry is rapidly growing, where an increasing majority of top firms are investing heavily in Big Data technologies, while it is also critically important to the operations of their firms. Big Data has become the new “corporate standard,” whereby the outcomes it produces and the business proficiencies it enables is prioritized. Due to its ever expanding demand and value, Big Data as a service market (BDaaS) is also rapidly growing and involves the outsourcing of the wide variety of end-to-end Big Data mining functions within the cloud as well as ongoing support services. As reported by Forbes in 2015, it is estimated that the global Big Data market will be worth $88 billion by 2021, while its auxiliary BDaaS market could be worth $30 billion. According to PriceWaterhouseCooper, venture capital investing is booming within the software industry, with most of the money being poured into big data analytics. According to industry insider Christopher Aderyeri, “Financial services businesses, including the investment banks, generate and store more data than any other business in any other sector…” As banking giant Goldman Sachs put it in 2015,

We believe the Data Revolution is here to stay, and that investors should recognize its potential to reshape the economic landscape. We believe the changes wrought by the Data Revolution will continue to ripple across industries–separating winners from losers, based on those who can best use data as an advantage–including in the world of investment management.

Fundamentally, Big Data serves a risk detecting and reduction function for investment banks. It enables their data analysts to instantly assess the impact of potential of escalating geopolitical risk on their assets and securities markets. With Big Data, banks now have built-in systems that map out market-shaping past events as a means to identify future patterns and risk.

Customer Relationship Management (CRM), also referred to customer intelligence or customer analytics, pioneered the “personalization” and customer-centered approach to consumer engagement in industry and financial markets. In doing so, according to technology company Invoca, CRM disseminates the narrative that “a better customer experience is driven by data.” Shannon Gerard, a technology company marketing manager, explains to industry insiders,

…customers are telling you what they want with every click, like, share, download, and call. Marketers have access to huge volumes and varieties of data. There are digital marketing channel data points (like web conversion rates, click-through-rates, open rates, online visits, keyword searches), transactional data (like credit card information and purchase value), and customer data (like region or city, age, gender, phone number, and phone type). With every marketing activity you have the opportunity to capture almost limitless data.

CRM’s personalized marketing and customer-centered business model requires an enhanced 360-degree (or complete) view of individual and groups of customers in very intimate ways. This means mining all available data from all available sources about customer’s behaviors, and employment and personal lives as a means to shape long-term customer loyalty to increase market share (profits). To do so, CRM systems seek to capture customer data inside and outside of a given company and apply descriptive, predictive, diagnostic and prescriptive analytics that generate demographic, behavioral and psychographic insights. In doing so, a complete and complex profile of a customer’s ecosystem and spheres of influence are created by identifying customer’s social communities, family, friends and coworkers; employment history; lifestyles; social activities; political views; personal tastes and interests; group memberships, etc. Advanced analytics applied to social media and other forums are also being used to identify users that are “thought leaders” (or influencers) and users that are followers, while also determining the relative strength of the leader on a particular topic or site. In the world of CRM, this allows businesses to both glean marketing trends from leaders as well as to target them more specifically with marketing campaigns. Outside of CRM purposes, identifying and targeting “thought leaders” can clearly serve more authoritarian purposes.

When writing about the advantages of personalization and CRM, industry insider Ramon Ray cautioned his industry peers that the associated privacy invasions can be perceived as “creepy.”

In the same vein, FinTech, according to Deutsche Bank, “is a term that defines the digitization of the financial sector and is a catchall term used for advanced internet- and cloud-based technologies in the financial sector.” Built into this, and most relevantly, FinTech describes small and large financial firms use and investments in innovative Big Data analytic technology to “personalize” their customer engagement, trading and risk management activities. According to Matt Turner of Business Insider, “Goldman Sachs is going big on big data.” Turner goes on to report that both Goldman Sachs and JP Morgan are investing “deeply” in artificial intelligence and deep learning. Quoting Goldman Sach’s Don Duet, Turner reports, “It’s a very important both technological strategy for the firm as well as business strategy and helping us move to a better degree of data-driven businesses as well as really deriving expertise, content, and knowledge of information.”

Lars Hamberg, a portfolio manager at AFAM Funds, points out that financial firms have used data to inform their decisions for quite a while, yet the tipping point came with a breakthrough “when computers started learning how to read.” Hamberg pointed to early financial industry experiments in using sentiment analysis with social media, with “so-called Twitter hedge funds,” which were not successful and caused many within the world of finance to “give up” on exploiting data in financial markets. As Lumley went on to report, Hamberg asks, “Why is it that big media companies like Google are the frontrunners in behavioral analytics and big data? Banks know everything about their customers. The financial sector has been filing away info on us for years and yet they do nothing with it.” Hamberg’s rhetorical question was speaking to how technology giants like Google, Apple, Facebook, Amazon, Alibaba and a host of new start-ups took the lead (post 2008 crisis) in redefining the finance industry and its customer engagement practices with data mining technology.

According to the powerful global management consulting firm, McKinsey & Company, “In a world where more than 90% of data has been created in the last two years, FinTech data experiments hold promise for new products and services, delivered in new ways.” To do so, McKinsey claims that Fintech offers “fully personalized” real time customer engagement via smartphones and tablets armed with applications that have access to unprecedented amounts of personal data. FinTech startups, large consumer technology ecosystems like Facebook, Google, Apple, Amazon, Netflix, etc. and innovating long existing financial firms powered by Big Data analytics are, as McKinsey reports, “opening up new [market] battlegrounds in areas like customer acquisition, customer servicing, credit provision, relationship deepening through cross-sell, and customer retention and loyalty.” More broadly, and as with CRM, this means FinTech is:

Building a comprehensive data ecosystem to access customer data from within and beyond the bank; creating a 360-degree view of customer activities; creating a robust analytics and data infrastructure; and leveraging these to drive scientific (versus case law-based) decisions across a broad range of activities from customer acquisition to servicing to crossselling to collections – all are critical to a bank’s future success (McKinsey & Company).

As Steven Ramirez from the tech firm Beyond the Arc exuberantly exclaims, “Think about all that text-based data available from customers’ social media comments, postings on support forums, call center notes, chat sessions, complaints, and in-app feedback.”

In the financialized global economy, securitized debt is the new currency and generator of mass wealth. As part of the vast Big Data and IoT ecosystem, FinTech promises to more efficiently exploit debt-based services via: equity platforms for crowd funding; platforms that connect lenders with borrowers; data visualization tools that assist in following companies, suppliers and clients; and a range of debt payment systems based in mobile and cloud technologies. According to McKinsey & Company, the strategy that enable these activities are readily in place:

Two iPhone 6s have more memory capacity than the International Space Station. As one FinTech entrepreneur said… “I can scale a business on the public cloud. There has also been a significant demographic shift… 85 million Millennials, all digital natives, are coming of age, and they are considerably more open… to considering a new financial services provider that is not their parents’ bank.

Big Data analytics is also empowering the financial industry with the opportunity to predict “next best actions” in terms of “customer needs” and investment strategies that expedite securitization of debt. McKinsey goes on to report, “the most exciting area of FinTech innovation is the use of data” to innovate lending practices, especially “with new credit scoring approaches – ranging from looking at college attended and majors for… students with thin or no credit files to trust scores based on social network data.” With the ability to analyze an endless sea of data, FinTech ensures that the financial industry has more information, and therefore more “personalized” control over the indebted masses (“customers”).

Data as Counterintelligence for Policing as Counterinsurgency

Big Data is also at the heart of the marriage between state and private security and surveillance systems and high tech weaponry, which can be readily activated to either pacify, coopt or violently suppress resistance movements. Just one dimension of this apparatus was revealed in 2013 when Edward Snowden exposed the U.S. National Security Agency’s PRISM program, which entailed Google, Yahoo, Apple, Facebook, Microsoft, Skype and others giving the NSA access to their customer’s activities, including search histories, posts, emails, file transfers and video and audio chats. Since this revelation, the same companies have waged a PR campaign to clear their reputations, while still appearing to quietly work to participate in the same practices. Current surveillance debates are focused on encryption, where federal law enforcement is demanding that technology corporations build “backdoors” into their products so that state and federal investigators can read and listen to “criminal suspects” encrypted communications.

In April 2016, it was reported that audio and video recording technology is increasingly being used on public and private bus and train systems throughout the U.S., funded by the federal government and subsidizing the private security industry. According to National Public Radio, “It’s not clear how many… transit agencies are doing this. But the answer seems to be a lot. The cost of surveillance systems can run into the millions of dollars, which is often covered by the Department of Homeland Security.”

Along those lines, Edward Snowden and others have also revealed how Big Data is being used by governments and the private sector for familiar purposes – to specifically monitor and track activities deemed to be dissident in nature (such as Black Lives Matter activism). The difference now is that it is happening in more comprehensive and “personalized” ways.

Currently local, state and federal agencies are using complex data software to identify everything from suspicious Internet addresses and metadata associated with fraudulent tax filings to automatically gathering traffic data via driver smartphone apps through formal partnerships between google and city governments. Yet the volume, velocity, variety and veracity of these data-driven strategies are much more ominous. In 2008 Mike German and Jay Stanley of the American Civil Liberties Union (ACLU) wrote:

If the federal government announced it was creating a new domestic intelligence agency made up of over 800,000 operatives dispersed throughout every American city and town, filing reports on even the most common everyday behaviors, Americans would revolt.

In the wake of 9/11 in 2003, as the U.S. was invading Iraq and ramping up the never ending ‘War on Terror,” the federal government established such a strategy, which was updated and outlined by the Secretary of Homeland Security Janet Napolitano in 2013, which in part reads:

We have learned as a Nation that we must maintain a constant, capable, and vigilant posture to protect ourselves against new threats and evolving hazards. Ensuring all of those who protect the Homeland have and share the necessary information to execute our missions… [o]ver the past two years, the Department has been working diligently with our homeland security partners to build a new architecture to execute our missions. The four essential elements of the distributed homeland security architecture-The National Network of Fusion Centers, the Nationwide Suspicious Activity Reporting Initiative, the National Terrorism Advisory System, and the “If you See Something, Say Something™” campaign-learn from and build on each other.

Within this solidifying “architecture” Fusion Centers are on the front line of mining and sharing the private data of millions of U.S. citizens and residents within all realms of the state-finance matrix; making them a centerpiece and powerful hub of the Big Data and Internet of Things ecosystem (ACLU, 2016). In their role, according to the ACLU, Fusion Centers were designed to consolidate,

…localized domestic intelligence gathering into an integrated system that can distribute data both horizontally across a network of fusion centers and vertically, down to local law enforcement and up to the federal intelligence community. These centers can employ officials from federal, state and local law enforcement and homeland security agencies, as well as other state and local government entities, the federal intelligence community, the military and even private companies, to spy on Americans in virtually complete secrecy.

According to the U.S. Department of Justice, Fusion Centers also exchange data with “foreign partners.”

The ACLU goes on to point out that within the context of “the nation’s long history of abuse with regard to domestic ‘intelligence’ gathering at all levels of government,” Fusion Centers are characterized by ambiguous and unaccountable chains of command, extreme secrecy, “troubling private-sector and military participation, and an apparent bent toward suspicionless information collection and data mining.” While portrayed as necessary in keeping law abiding citizens safe from terrorists and violent criminals, these strategies fundamentally serve as a highly sophisticated authoritarian infrastructure.

As previously reviewed, the use of authoritarian counterintelligence operations to undermine domestic dissent is an American tradition. Yet, domestic counterinsurgency (COIN) also has a strong legacy throughout U.S. history. Native dispossession and genocide in all forms, including the history of boarding schools and ongoing erasure, are an integral part of the structure of settler-colonialism and the ongoing domestic counterinsurgency strategy that supports it. Also, as William Y. Chin reminds us:

The history of America is a history of enduring conflict between black insurgents and white counterinsurgents. This conflict began centuries ago with the forced transport of enslaved blacks to America’s shores. From the beginning, whites employed all levers of national power including laws to suppress black resistance. The laws became counterinsurgency weapons launched against blacks in an internal conflict lasting generations.

With this understanding, insurgency (or insurrection and rebellion) can be described as an organized and protracted political and/or armed struggle in response to systems of domination. Insurgent struggles normally have emancipatory objectives and seek to obtain recognition rights or power within an existing government or by removing an occupying or colonial power. The origin story of the U.S. Revolutionary War is described as an insurgent struggle. Counterinsurgents are normally the agents of those in power and from their position insurgents are criminals, terrorists, “the Other,” and therefore “the enemy” to be eliminated or contained. COIN strategies utilize a spectrum of authoritarian methods to undermine or defeat an insurgency, including: military, paramilitary, political, legal, economic, ideological, counterintelligence and surveillance strategies. The Big Data, Deep Learning and the Internet of Things serves as the counterintelligence infrastructure for the permanent counterinsurgency wars of the twenty-first century.

As documented in the book Law Enforcement in the United States, the model of professional municipal policing in the US, which dates back to the early 19th century, was preventative in nature and served the purpose of suppressing “crime and riot.” Accordingly, the authors claim that police “were to actively seek out trouble” before it disrupted social order.

The contemporary dominant narrative in the U.S. tells us the purpose of federal, state and local law enforcement is to “keep the peace” by enforcing domestic laws, protecting the rights of citizens (and alleged criminals) and to resort to violence as a matter of last resort. This model of policing does exist in the U.S., but it is only applied to the nations white opulent minority. Military forces, on the other hand, are intended to engage in combat and destroy external enemies of the United States. Over the past four decades, while still true to its origins, the militarization of domestic law enforcement as a COIN strategy has coincided with the rise of neoliberalism and the onset of its three intersecting and permanent wars: the “war on drugs” the “war on terrorism” and the “war on immigrants.” As with policing throughout U.S. history, these “wars” are constructed under the domain of white supremacy and maintained by the material conditions of austerity. Thus, policing as COIN has resulted in a hyper-racialized and hyper-militarized model of law enforcement on a national level. Within this domain, domestic law enforcement has taken on the look, attitude and actions of combat troops who are tasked with carrying out counterinsurgency missions against Black, Brown and Indigenous insurgents, treating their impoverished and segregated communities as occupied territory under martial law.

Military-style training now prepares law enforcement for everyday engagement within domestic insurgent territory, while military weapons (assault rifles, riot gear, and body armor to tanks, grenade launchers, and armored vehicles, etc.) and tactics remain at the ready. When domestic insurgents actively resist their subjugation (even in Constitutionally protected ways), or engage in other “suspicious activities;” law enforcement agencies deploy their “special operation” (SWAT) teams that are now akin to Army Rangers. Writing about the police response to protests over the police murder of Michael Brown in Ferguson Missouri, Glenn Greenwald described the police tactics as a, “blatantly excessive and thuggish response to ensuing community protests… that resembles an occupying army.” Greenwald went on to note:

But none of this is aberrational. It is the destructive by-product of several decades of deliberate militarization of American policing, a trend that received a sustained (and ongoing) steroid injection in the form of a still-flowing, post-9/11 federal funding bonanza, all justified in the name of “homeland security.”

To defeat and control domestic insurgents, U.S. law enforcement regularly engages in a range of tactics that include the establishment of no-fly zones (to restrict media coverage), curfews and checkpoints, media-messaging, arbitrary search, seizures and detainment, home invasions and the use of Tasers, chemical weapons and explosives. They also readily resort to physical assault and summary executions. In an unprecedented dual use of militarized technology and ordinance, in 2016 the Dallas Police Department used a robot to deliver and detonate a bomb to kill a Black male suspect in the shooting of several police officers during a protest over the ongoing police murders of Black men.

While friendly sounding, “community policing” is also a fundamental COIN strategy that serves an intelligence-gathering purpose. The idea is based on police having frequent interactions with “low-level offenders,” to forge bonds within insurgent neighborhoods and to build partnerships with businesses, schools, community organizations and other service providers. As investigative journalist Aaron Cantú puts it, “The more inroads police have into a community, the thinking goes, the more likely they are to intercept valuable tips about “criminals and extremists.” The NYPD’s “broken windows” policing is based on this strategy. Community policing is an essential “eyes on the ground” surveillance strategy for culling invaluable data that is fed into the network of data mining Fusion Centers across the country. In her article The other side of the COIN: counterinsurgency and community policing, Kristian Williams claims that other “police innovations that COIN theorists recommend for military use include: the Neighborhood Watch, embedded video, computerized intelligence files, and statistical analysis.” While U.S. law enforcement has adopted much from the military when it comes to COIN operations, it was the military that learned from a U.S. police department when it came to implementing “community policing” strategies in Afghanistan and Iraq. Williams describes how in 2010, in preparation for deployment to Afghanistan, 70 Marines spent a week accompanying LAPD officers to learn the basics of anti-gang (insurgent) investigation tactics and methods to build rapport within “insurgent” communities.

Examples of the domestic COIN based counterintelligence strategies are too numerous to document here, but the following examples are both illuminating and disturbing. New York City makes use of social media and traffic data to assist police to establish probable cause tied to a “digital stop-and-frisk” practice, which in its original form is notorious for targeting Black and Brown people. A partnership between the LAPD and Motorola Solutions was established in 2010 to monitor a large public housing project with advanced biometric surveillance technology. As covered in La Weekly in 2014, “[LAPD] chief Bratton called it the start of an ambitious buildout to use remote ‘biometric identification, which can track individuals citywide.” Hamid Khan, who works with the Stop the LAPD Spying Coalition, claims the Los Angeles Police Department is using advanced technology to create “a massive architecture of surveillance, spying and infiltration.” According to the coalition’s website, the LAPD (along with other cities) surveils the city’s residents by using: stingrays and DRT boxes (technologies that can jam or intercept calls and text messages from hundreds of cellphones simultaneously); street cameras equipped with facial recognition technology; aerial drones; robots, police body cameras with tracking abilities and license plate readers. License plate readers are small yet high-speed cameras that are used throughout the country and are mounted on police cars, traffic signals, road signs, bridges, apartment complexes, schools, bus stops, shopping centers, businesses and more. Readers can photograph thousands of license plates per minute, while recording the date, time, and location of every scan. The technology is largely owned and operated by private companies that then sells the data to an array of customers, including law enforcement agencies. According to CNN, “Community surveillance 2.0 is now all about huge data mash-ups and incredible software that quickly sorts through mountains of information. Bottom line: A relatively small number of people have easy access to data that can track your whereabouts.”

As noted previously, the web of domestic COIN counterintelligence structures of which Fusion Centers are a centerpiece, also includes the Nationwide Suspicious Activity Reporting Initiative, which is an integrated system for reporting, tracking, and accessing “Suspicious Activity Reports” made by law enforcement and the public (“If You See Something, Say Something”). According to the Electronic Privacy Information Center, within this structure, “suspicious activity” includes “vandalism, photography, and questioning individuals ‘at a level beyond mere curiosity’ about facility purpose or security procedures.” As reported by Julia Craven in the Huffington Post, “suspicious activity” can also include activities interpreted as “suspected pre-operational surveillance” [like the use of cameras or binoculars], counter-surveillance efforts [changing one’s direction, erratic driving or altering one’s appearance], and taking measurements or counting footsteps [or merely looking down at one’s feet]. Hamid Khan goes on to reinforce the point that “Any of these innocuous behaviors can lead the police to write up a secret file on an individual and upload it into a database accessible to every law enforcement agency in the country.” Craven reports that a 2015 inspector general’s audit of the Los Angeles Suspicious Activity Reporting program found, “Over 30 percent of suspicious activity reports involved black Los Angelenos and 50 percent of the women surveilled were black… Black people comprise 9.6 percent of the city’s population.”

This cornucopia of data being collected and mined through all of these surveillance technologies, and largely operationalized by the targeting of subordinated groups as “insurgents,” is also the basis for a national surveillance strategy known as “predictive policing.” Straight out of the movie and series “Minority Report”, agencies apply deep learning predictive analytics to this mountain of data for the purpose of predicting future crimes.

In his article in The Verge, Matt Stroud reports that in a bizarre incident in 2013, a Chicago Police Department commander showed up out of the blue at the home of a 22-year-old Black male named Robert McDaniel to warn him that he is being watched and threatened major consequences if he commits any future crimes. McDaniel did not have a violent criminal history, yet lived in an impoverished Chicago neighborhood and did not finish high school. Demographically, everything about McDaniel made him an ideal candidate for this form of data profiling. Shocked by the encounter, McDaniel would later tell the Chicago Tribune “I haven’t done nothing that the next kid growing up hadn’t done.” McDaniel soon found out that he, like 400 other Chicago residents were on the city’s predictive policing program known as the “heat list.” As a means to forecast crime, data gathering and predictive analytic programs in Chicago target “hot people” (Black, Brown and poor) in “hot spots” (impoverished neighborhoods) using years and even decades’ worth of crime reports and other types of personal data that is mined within the Big Data-Internet of Things ecosystem. Predictive analytic algorithms, according to Nate Berg of The Guardian, then identify areas, groups and individuals that are deemed to have “high probabilities for certain types of crime, placing little red boxes on maps of the city that are streamed into patrol cars.” As part of the violent dehumanizing narrative embedded within white supremacy, Berg goes on to report how LAPD Captain John Romero, “likens the process to an amateur fisherman using a fish finder device to help identify where fish are in a lake. An experienced fisherman would probably know where to look simply by the fish species, time of day, and so on.” As reported by Stroud, Commander Jonathan Lewin of the Chicago Police Department claimed in 2014, “This [program] will become a national best practice. This will inform police departments around the country and around the world on how best to utilize predictive policing.” Yet, as the deep learning scientist Michael Wu clarified earlier, predictive analytics does not predict one potential future, but “multiple futures” centered on a decision-maker’s preferred actions.

Big Data is also rapidly changing political analysis and communication, whereby rich records about our lives – polls, voter registration, credit-card data and much more – assist lobbyists and campaign managers to effectively target those of us who will donate and show up to vote. As Phil Howard reported in Politico, Big Data also enables party strategists to do in-house research and experimentation on the “mid-spectrum, undecided or ideologically ‘soft voters’ to see what kinds of contacts and content will attract new supporters.” Phil Howard takes it further, claiming:

[The] Internet of Things will be the most powerful political tool we’ve ever created. For democracies, the Internet of Things will transform how we as voters affect government — and how government touches (and tracks) our lives. Authoritarian governments will have their own uses for it, some of which are already appearing. And for everyone, both citizens and leaders, it’s important to realize where it could head long before we get there.

Mining the “Solopreneurs” of Tomorrow

Understanding all that encompasses Big Data is essential to recognizing how its associated technology serves as surveillance infrastructure; intended to shape how humans think, feel and behave as neoliberal subjects, to safeguard financial markets and further enrich elite investors and to preserve the existing social order. Returning to my earlier question concerning the infusion of personalized learning into education in which I asked, “how is personalized learning personal?” The answer: Big Data defines personalized learning and Big Data’s “Deep Learning” analytics ensures that all personal information about a student is known and to be exploited.

While social control is often considered to be one of the primary purposes of schooling, in the age of neoliberal financialization, this purpose is being taken to new heights through the instruments of education technology (EdTech) as part of the Big Data infrastructure. Fundamentally, the primary function of EdTech within this landscape is intended to build and reinforce schooling as a structure of social control as part of the all encompassing Big Data/Internet of Things surveillance ecosystem. To do this, digital education software products on tablets, laptops, mobile devices, wearable technology and more enable deep learning analytics and artificial intelligence systems. Within this environment, teachers function as highly disciplined data technicians tasked to monitor student behavior and compliance. The revolution in education that the EdTech industry and education reformers promise will allegedly empower students and teachers while remedying social inequities through the use of technology that, according to Jesse Irwin,

…is being used to track and record every move students make in the classroom, grooming students for a lifetime of surveillance and turning education into one of the most data-intensive industries on the face of the earth. The NSA has nothing on the monitoring tools that education technologists have developed in to “personalize” and “adapt” learning for students in public school districts across the United States.

The revolutionary venture philanthropist Bill Gates has advanced a $1.1 million-plus biometric sensor project that would equip children with Galvanic Skin Response (GSR) bracelets as a means to measure student engagement. As captured in a folksy TED Talk called “Teachers Need Real Feedback”, Gates is also advancing a $5 billion project to install video cameras in all classroom to record teachers for the purposes of evaluating their performance. The recordings would then be evaluated by distant contracted evaluators using a check list of teaching skills to check off as they watch.

The imposition of EdTech products throughout education are also reinforced by well worn education reform narratives, a principal one being that increased competition in global labor markets, coupled with an inequitable “skills gap,” can only be addressed through a “digitally rich” social efficiency model of education. Within this workforce development model of education, narrow standards of competency are prescribed by, and serve the interests of, financialized capitalism; thus rationalizing neoliberal reforms to instill the “21st century skills” that are required of students as future workers and consumer citizens. These are the interests by which education is being realigned via EdTech to fulfill its original mission, marketed as the determinant of success based on self-determined vocational choices, which define student achievement, the value of credentials and employment opportunities. A glaring example of this comes from the National Network of Business and Industry Associations, a trade organization that represents major industry sectors and is sponsored by the Business Roundtable. Its members include the manufacturing, retail, health care, energy, construction, hospitality, transportation and information technology sectors; as well as venture philanthropists, including the Walmart Foundation. A 2014 policy publication put out by the Network titled, “Common Employability Skills: A Foundation for Success in the Workplace: The Skills All Employees Need, No Matter Where They Work,” proclaimed:

Today, employers in every industry sector emphasize the need for employees with certain foundational skills. This model can take its place as the foundation for all industries to map skill requirements to credentials and to career paths. In doing so, this model allows employees to understand the skills that all industries believe prepare individuals to succeed. Educators and other learning providers will also have an industry-defined road map for what foundational skills to teach, providing individuals the added benefit of being able to evaluate educational programs to ensure they will in fact learn skills that employers value.

These “industry-defined” skills include “applied skills” grounded in the disciplines of science, technology, engineering and math (STEM); along with basic reading and writing skills. This includes the capacity for critical thinking, similar to how a scientist or mechanic can hypothesize and work through concrete problem solving steps. As the National Network of Business and Industry Associations describes it, industry is also seeking “personal” and “people” skills that are akin to being a soldier, through training that fosters loyalty and discipline to a mission, where “integrity, initiative, dependability, adaptability, professionalism, teamwork, communication and respect” are ingrained. Workplace skills are naturally important too in terms of planning and “organization, decision making, business fundamentals, customer focus and working with tools and technology.” According to the company New World of Work, the development of these skills via “personalized learning” promises to efficiently determine which students will be “the solopreneurs of tomorrow” with the understanding that:

Gone are the days of the 40-year career with a guaranteed pension. The workplace of today and tomorrow is not necessarily a place at all. It is a virtual nexus of collaborators across the globe with varied projects; requiring different skill sets at different times. Tomorrow’s workers will need to be agile, financially savvy, entrepreneurial in their approach to work and how to market themselves to the world, resilient, and comfortable in their own self-understanding.

This vision of “tomorrow’s” workforce is not intended for everyone of course, only those who will “add value” to the cultural political economy of global finance. Within this landscape, the deceptive market-based empowerment discourse of personalization, self-determination and choice are deeply embedded. Yet this model is insidiously akin to students being mice within a Skinnerian lab’s maze, forced to find their own way to one predetermined exit, while being monitored and evaluated the entire way. Those who have the right “hard” and “soft” skills to make it through the maze are deemed to be superior and allowed to live, while those who do not are deemed to be disposable. Ultimately, within the digitized personalized and competency-based model of education, the immense capacity for tracking and sorting students would make early social control theorist Edward Ross and social efficiency guru John Franklin Bobbitt burst with envy. Especially in that the ideologies of Social Darwinism and Eugenics are fundamentally embedded throughout.

As far back as 2000, a Bloomberg posting, titled “The Explosion in E Learning,” claimed, “Dozens of new companies are springing up to serve the emerging K-12 market for digital learning. Investors have poured nearly $1 billion into these companies since the beginning of 1999, estimates Merrill Lynch.” In 2005 a national Data Summit was convened by the Council of Chief State School Officers and the US Department of Education to kick off a Data Quality Campaign, a concerted national strategy “to improve the quality, accessibility and use of data in education.” Supported by the Bill & Melinda Gates Foundation and managed by the National Center for Educational Accountability (a pioneering education reform data company), the summit was attended by a who’s who of private sector education reform companies, who committed to “working together to… encourage and support state policymakers to: ‘Improve the collection, availability and use of high-quality education data, and Implement state longitudinal data systems to improve student achievement.’”

This long-term effort has since resulted in the federal government mandating every state to collect personal student information in longitudinal databases, known as the Student Longitudinal Data Systems (SLDS). As reported in the Washington Post in 2015, with the SLDS,

…the personal information for each child is compiled and tracked from birth or preschool onwards, including medical information, survey data, and data from many state agencies such as the criminal justice system, child services, and health departments… their data more easily shared with vendors, other governmental agencies, across states, and with organizations or individuals engaged in education-related “research” or evaluation — all without parental knowledge or consent.

More recently federal grants are being extended to states to expand these efforts, including making it easier to share data through multi-state data exchanges. In fact, according to the Washington Post, the federal grants require recipient “states to collect and share early childhood data, match students and teachers for the purpose of teacher evaluation, and promote inter-operability across institutions, agencies, and states.”

This unleashing of the EdTech industry – along with other financializing and privatizing mandates – on U.S. public education have largely been facilitated by federal policy and enacted by state legislatures. The first was the 2002 No Child Left Behind Act (NCLB) and was largely implemented by states under the threat of withholding federal funds intended for impoverished families. NCLB was followed by the 2010 Race to the Top (RTTT) competition, which further unleashed data-driven surveillance systems into public schools. RTTT’s digitized Common Core curriculum and its associated online tests are well known for accumulating huge amounts of personal student data across state borders and sharing it with third parties, including the financial industry. Immediately following the 2008 financial crisis, RTTT offered large grants to debt ridden states contingent upon them passing an array of punitive education reform policies. Drafted by industry and venture philanthropist, NCLB, RTTT and other polices are also enacted by state governments at the behest of industry demands and lobbying. More recently the U.S. Department of Education began to encourage states and school districts to adopt deep learning (“personalized learning”) systems by offering waivers from rigid NCLB rules.

The National Education Policy Center reports that in 2010, the Foundation for Excellence in Education convened the Digital Learning Council (a group comprised of over one hundred leaders in the education reform industry), which included “government, philanthropy, business, technology and members of policy think tanks led by Co- Chairmen Jeb Bush, and West Virginia Governor Bob Wise.” Following an American Legislative Exchange Council (ALEC) template, the group drafted the 10 Elements of High Quality Digital Learning, a comprehensive outline of policies and actions for state legislatures to follow in integrating EdTech into K12 public education. In 2015 Congress revised NCLB by passing the Every Student Succeeds Act (ESSA), which advances funding for EdTech generally and personalized learning specifically.

The ongoing ushering in of personalized learning into schools – via the deeply intrusive capacities of Adaptive Learning Systems – is being positioned to replace the current use of state mandated tests as student, teacher and school accountability systems (outcomes-based education) with an even more insidious competency-based education (CBE) model. Within this model, high stakes assessments occur every day throughout the day, promising to undermine current efforts by public education activists to center a resistance movement on parents and students “Opting Out” of education reform mandated tests. Alarmed by this data landscape, progressive education author Alfie Kohn claims:

Still more worrisome are the variants of ed tech that [are] putting grades online (thereby increasing their salience and their damaging effects), using computers to administer tests and score essays, and setting up “embedded” assessment that’s marketed as “competency-based”… [using] dystopian devices that basically test kids (and collect and store data about them) continuously… “to do in nanoseconds things that we shouldn’t be doing at all.”

The competencies of CBE within personalized learning are not earned by credit hours completed, but instead by students working independently to complete a sequence of digitized and tracked exercises that lead to a “badge of completion.” Once such badge (a product of the multinational corporation Pearson) is the “Grit Badge” that assesses “Growth, Resilience, Instinct, and Tenacity.” As Pearson describes it, Grit Badges are an instrument that “demonstrates a strong correlation of GRIT and several key success factors” including “desire to improve one’s station in life, effort, employability, goal completion, goal magnitude and income.” This grit narrative is embedded within a larger education reform storyline that reinforces the myth of American meritocracy; is largely used in reference to Black and Brown boys and implicitly attached to a deficit label that reinforces the ideology of Eugenics. In the world of personalized learning, these (merit) “badges” are the new credential for the self-reliant “solo worker” in the so called “gig economy” (yes, like a musician doing a gig). The gig economy is intrinsic to neoliberal financialization, in which the drive to reduce labor costs as a means to maximize profits results in greater worker insecurity and reduced wages and benefits within a society void of social safety nets. This “liberates” workers to become temporary “solo” workers and “independent contractors” within highly profitable companies that make up the digitized “sharing” economy (Uber, Airbnb, TaskRabbit, etc). According to a recent study, by 2020 forty percent of U.S. workers will be independent solo workers attempting to piece together a series of “gigs” to survive. As the Pearson corporation frames it:

Alternative learning credentials including college coursework, self-directed learning experiences, career training, and continuing education programs can play a powerful role in defining and articulating solo workers’ capabilities. Already badges that represent these credentials are serving an important purpose in fostering trust between solo workers, employers, and project teams because they convey skill transparency and deliver seamless verification of capabilities.

True to the American tradition of myth making in the service of ideology, Competency-Based Education and its personalized learning narrative is compelling. Particularly since it plays on the fundamental American values of individualism, meritocracy and grit, while offering hope of providing greater opportunities for employment and freedom from the tyranny of bosses within the bleak landscape of austerity. As such, to be a winner within this dog-eat-dog “Wild West” economy, students as future solo workers are expected to show “true grit” and have the “right stuff” in order to endure an unforgiving financialized world.

Personalized learning is also (conveniently) confused with the empowering pedagogical practices associated with traditional theories of personal and student-centered learning, which are deeply relational, actively collaborative, humanistic, creative and based in intellectual discovery and critical inquiry. Instead, personalized learning and its competency-based model relies on prefabricated skills-based exercises based on a student’s data “profiled” competencies as determined by adaptive learning analytic software. As Canadian scholar Philip McRae points out, personalized learning does “not build more resilient, creative, entrepreneurial or empathetic citizens through their individualized, linear and mechanical software algorithms… [and instead] are reductionist and primarily attend to those things that can be easily digitized and tested.”

A Learning Management System (LMS) is the web-based education platform, which functions as an essential part of EdTEch infrastructure and oversees the integration of curriculum, instructional resources and assessment strategies in both K12 and higher education. As Phillipo and Krongard claim in their marketing publication, “Learning Management System (LMS): The Missing Link and Great Enabler,” LMS’s “tie together contemporary education reforms with effective and creative uses of technology.” More importantly, LMS’s facilitate learning analytics and data mining systems that profile, track, monitor and shape behavior relating to student performance, teacher productively and institutional success related to predetermined learning outcomes. There are currently hundreds of LMS platforms to choose from, most of which are integrating with major social networking sites and are increasingly cloud-based. Data mining generally, as well as through EdTech, uses machine/deep learning analytics to build user profiles based on the continuous collection of data that describes individual users’ background, needs, preferences and interests. Learning analytics is built into LMS systems and borrows analytic technology intended to profile and analyze consumer activities, identify trends, and predict consumer behavior. According to the technology industry association, the New Media Consortium:

Education is embarking on a similar pursuit… learning analytics is already starting to provide crucial insights into student progress and interaction with online texts, courseware, and learning environments used to deliver instruction… [through] mobile and online platforms that track data to create responsive, personalized learning experiences.

Learning analytics enables user modeling and is a fundamental component of Adaptive Learning Systems, or “the new teaching machines.” According to a 2012 U.S. Department of Education brief, user modeling analytics through EdTech cohere with surveillance-based accountability systems within education reform by encompassing,

…what a learner knows, what a learner’s behavior and motivation are, what the user experience is like… At the simplest level, analytics can detect when a student in an online course is going astray and nudge him or her on to a course correction. At the most complex, they hold promise of detecting boredom from patterns of key clicks and redirecting the student’s attention. Because these data are gathered in real time, there is a real possibility of continuous improvement via multiple feedback loops that operate at different time scales—immediate to the student for the next problem, daily to the teacher for the next day’s teaching, monthly to the principal for judging progress, and annually to the district and state administrators for overall school improvement.

As with all EdTech products, the marketing of adaptive learning software is replete with terms like “algorithms” and “predictive analytics” that promise to roll in an equitable education utopia through the disruption of outdated teaching practices. Yet, as is pervasive in the EdTech and education reform industry, there is no evidence to support their claims (as detailed below). Furthermore, its products are proprietary and therefore lack transparency and are attached to fine-grained and commodified data mining scheme that is brimming with privacy violations.

Intelligent Tutor software, according to EdTech industry insider Barbara Kurshan, is an Adaptive Learning System that is able to track the “mental steps” of learners when they are engaged in problem-solving tasks as a means to diagnose “misconceptions” so as to evaluate learners understanding of subject matter. Kurshan also notes how Intelligent Tutor Systems offer “timely guidance, feedback and explanations to the learner and can promote productive learning behaviors, such as self-regulation, self-monitoring, and self-explanation.” It then prescribes content (curriculum) and learning activities (pedagogy) based on a learner’s diagnosed level of difficulty. According to Kurshan, “[t]hese systems are also able to mimic the benefits of one-to-one tutoring, and some of these systems outperform untrained tutors in specific topics and can approach the effectiveness of expert tutors.” Philip McRae warns how the “adaptive learning system crusade” in education is highly organized and is gaining momentum, driven by venture capitalists, private equity investors and multinational corporations such as Pearson, which invested over $3.5 billion into EdTech companies in the U.S. alone in 2014.

Adaptive Learning Systems are integrated into the comprehensive data mining capacities of LMS’s which are also being integrated with Student Information Systems (SIS’s). SIS’s gathers digitized data concerning demographic information (including income level, race and ethnicity), student records (including grades, test scores, disabilities and Individual Education Plans), medical and mental health history, attendance, disciplinary records and more. SIS’s generate a wealth of longitudinal data that was previously difficult to gather and consolidate. All together, these technologies have brought about a dramatic growth in computational power and storage capacities that allow for the gathering and housing of unprecedented amounts of data; intended to identify behavioral connections and patterns of students (and teachers) and allowing decision making engines to operate in real time learning systems.

According to education technology researchers Castro, Nebot & Mugica, the digitization of education via EdTech LMN’s has constructed an educational infrastructure that is based on massive amounts of information about teaching and learning interactions that are “endlessly generated and ubiquitously available.” In their study about the popular LMS program Moodle; Romero, Ventura & Garcia claim, “all this information provides a gold mine of educational data. As Leonie Haimson and Cheri Kiesecker reported in the Washington Post in 2015, “Remember that ominous threat from your childhood, This will go down on your permanent record? Well, your children’s permanent record is a whole lot bigger today and it may be permanent. Information about your children’s behavior and nearly everything else that a school or state agency knows about them is being tracked, profiled and potentially shared.”

As if channeling Ayn Rand, the notorious champion of free-market individualism, EdTech industry insiders market personalized learning by prioritizing the learning needs of individuals over concerns for the common good. Accordingly, and referring to personalized learning, Austin Martin of the EdTech company Mindflash claims “the time has come” for education leaders “to look at the individual rather than the organization as a whole.” Disturbingly, Martin goes on to explain:

Getting personal with learning content and delivery begins with gaining a better understanding of the learner’s needs, interests, aspirations, and goals. Companies and organizations now are taking a deeper dive into data and analytics in order to assess, provide feedback, and determine personalized content and delivery methods. The rise of Big Data and the ability to analyze learning patterns and trends all the way to the individual learner by combing through mountains or terabytes of data is the new way to go as each learner’s “digital trail or footprint” can leave critical clues as to what works, what doesn’t, and how to create specific personal content.

Martin goes on to back up this assertion by referencing the 2016 U.S. Department of Education brief titled: “Enhancing Teaching and Learning, Through Educational Data Mining and Learning Analytics.” The brief references the DOE’s 2010 National Education Technology Plan, which extols the virtues of the EdTech industry’s personalized learning mission:

When students are learning online, there are multiple opportunities to exploit the power of technology for formative assessment. The same technology that supports learning activities gathers data in the course of learning that can be used for assessment. As students work, the system can capture their inputs and collect evidence of their problem-solving sequences, knowledge, and strategy use, as reflected by the information each student selects or inputs, the number of attempts the student makes, the number of hints and feedback given, and the time allocation across parts of the problem.

As in all aspects of the larger digital world of Big Data and Internet of Things; the intention of personalized learning is all about comprehensive surveillance intended to penetrate deeply into all aspects of students’ lives to serve the interests of global financial markets. This model of personalization is facilitated by the EdTEch industry via the increasing integration of Adaptive Learning Systems (user modeling and Intelligent Tutoring Systems), Learning Management Systems, Student Information Systems; as well as MOOCS, Open Educational Resources, Flipped Classrooms, Clickers and all that falls under what is called “blended learning.” According to the multinational publishing corporation, Pearson:

Increasing student engagement is a goal in every school, and online and blended learning… allows schools to hold students accountable while keeping them engaged and motivated. Successful programs do much more than place technology in the classroom or students’ homes. Rather, flexible online and blended learning options allow districts to restructure traditional school models and provide data-driven and personalized instruction to improve learner outcomes.

As Philip McRae explains, “Children and youth should not be treated like automated teller machines or retail loyalty cards from which companies can extract valuable data.” In essence, the EdTech industry and financial firms have positioned themselves to have a reliable and extraordinary profit stream from the state in the name of “educating our children.” It begins with the continuous purchasing of the EdTech infrastructure, that ultimately leads to collected, stored, processed, analyzed, and “personalized” data being resold throughout the global finance industry.

With the capacity to significantly increase the volume, velocity, variety, veracity and value of data mining within schools, a highbred personalized learning platform known as Learning Relationship Management (LRM) is being positioned to fully-integrate student data from all possible sources. In doing so, LRM will replace LMS and SIS systems and further integrate student data across domains. By doing so, LRM seeks to reduce potential risk factors in terms of student progress, even at the front end when it comes to student admission decisions in selective K-12 schools (like charter schools) and in higher education. On message with other leading personalized learning “revolutionaries,” marketing research firm Wainhouse Research, claims that LRM’s will expedite the disruption of the “‘averagarian’ architecture of the existing system into one that values the individual student” through “granting credentials, not diplomas… replacing grades with a focus on mastering competencies; and… letting students determine their educational pathways.” LRM is also being marketed as facilitating community engagement, mentoring, coaching, career and alumni engagement functions byway of “productive” digitized relationships. Borrowing from the conceptual framework of Customer Relationship Managements systems, Wainhouse goes on to explain how LRM software offers “the ability to make data-driven decisions based on ongoing metrics that serve as meta-views into the school’s performance and micro-views into each learner’s progress.” According to the research firm Eduventures, LRM also provides,

…the utility of a central and scalable repository for learning, but also robust records management and an analytics engine capable of tracking individual learner progress, staging interventions when necessary, and mapping student progress to learning objectives and career outcomes. In other words, LRM offers a holistic student success solution that the education world has never before experienced.

A review of the marketing material of Fishtree, one of the leading Learning Relationship Management software companies, is illuminating. Combining adaptive learning with “the most incredible insight into student learning” through its “powerful performance analytics,” the Fishtree LRM system promises to make teaching more efficient and meaningful by providing a personalized learning experience that creates “the ultimate in digital instruction.” According to Fishtree, their LRM is the “ideal solution” for providing blended, flipped and project-based learning using online curriculum, open education resources and real-time content, while aligning them all with personal competencies and standards, including Common Core. Fishtree’s LRM claims to allow educators to “adapt to each learner’s needs with one click!” Fishtree guarantees teachers that it will also help them “differentiate and personalize” teaching with one “click of a button.” How so? Their LRM system makes

…the personalization process as easy as possible. Through our recommendation and personalization engines, each student using our system is offered resources adapted to suit his/her individual needs. This means every lesson and every assignment can be tailored to the needs of every single student. A teacher can then simply view student progress, and intervene at will. Personalized instruction has never been so easy!

Fishtree’s “time-saving platform” creates and delivers dynamic, personalized lessons so that teachers can “collaborate and interact with students safely and easily, monitor student progress consistently, and access all of this using any device, anywhere, at any time.” Furthermore, “Fishtree’s multitasking learning platform allows teachers to keep track of student progress easily and effectively” whereby “a teacher can simply assign activities at the click of a button, assess without having to intervene in any way, and track progress easily by viewing student performance through clear, informative graphs and charts.” Through Fishtree’s powerful analytics systems, teachers can see “if a student is not reaching the specified learning objectives, a teacher can intervene and reassess at will, with one click. This ensures all students reach their learning objectives at their own pace, while giving the teacher more control and making the reassessment process as simple as possible.” Additionally, as part of learning how to work as part of a collaborative team, Fishtree’s social stream feature, facilitates cooperation between students outside of the classroom, in real time, through their social media-based application, while giving teachers the ability to monitor all student activity.

The Proof is in the “Data”

Ultimately, industry interests peddle personalized learning as being “disruptive innovation.” Critics point out that disruptive serves as code for “dismantle” in that the mission of EdTech and personalized learning is to completely destroy public education and replace it with a thoroughly financialized authoritarian system. Within this system, education will be a privately operated, yet state subsidized (see charter schools) sector of the Big Data/Artificial Intelligence industry as an extension of the global financial industry. This (de)personalized model of education is a vastly controlled environment, void of meaningful human interaction, where children spend most of their time seated alone (often in cubicles) interfacing with devices that monitor and “adapts” digital materials based on the inputs clicked in by the child.

The EdTech industry’s profit making efforts to “reinvent” education is perpetually being propelled by a massive marketing and public relations campaign that permeates deep into society and is framed as an effort to forge a new era of enlightenment. A 2015 Market Data Retrieval (MDR) report titled State of the K-12 Market speaks to the inevitability of this era in that fifty percent of curriculum directors nationwide expect extensive “print-to-digital conversion” within the next three years, while over half of all school districts are now administering online assessments within their schools. MDR went on to claim, “These two intertwined aspects of education, linked by more rigorous Common Core Standards throughout the country, are reinforcing each other in this shift.” Accordingly, the Software & Information Industry Association, a major EdTech trade association, tells us that it is the efficacy of EdTech products that is driving this sanctified mission:

The evidence is strong that technology and eLearning are powerful tools for revitalizing education and preparing students for the world beyond the classroom. Pioneering schools have already shown what is possible when good education and good technology come together. Technology has repeatedly proven its power to energize and improve learning outcomes.

When one uncritically reads the majority of online publications about digital education associated with EdTech, the overall impression is that it is the inevitable magic bullet for improving student learning outcomes, college and career readiness and in closing the “achievement gap” (a term intended to ignore the existence of structural inequities). Questioning the effectiveness of EdTech products as the driving force of the EdTech market in the The Atlantic, Angela Chen reported, “every few months, a new study claims that gadgets in the classroom don’t improve learning—but that hasn’t stopped the educational technology market’s steady upward climb.” A review of the literature supports Chen’s claims in that there is very little, if any, credible evidence that EdTech products improve learning outcomes, according to any standards. More importantly, there is however mounting evidence that digitized technologies not only hinders learning in some areas, but is also significantly detrimental to child development. In fact, when claims are made that digital learning results in preferable or effective learning outcomes, it is often without credible supporting evidence or only supported by anecdotal evidence. Many of these claims are also advanced by studies that appear to be neutral institutional research scholars, yet in almost all of these studies, when digging a little deeper; institutional connections to the EdTech industry and/or education reform advocacy groups were found.

One example of this is a 2014 brief put out by the Alliance for Excellent Education and Stanford Center for Opportunity Policy in Education, which begins by acknowledging how “the introduction of technology into classrooms has failed to meet the grand expectations proponents anticipated.” The brief, titled Using Technology to Support At-Risk Students’ Learning, attempts to take a middle-ground while also advancing the interests of industry. It promotes the use of technology based on keeping teachers as trained professionals, yet training them to be active facilitators of diverse digital learning methods. Ultimately it promotes the EdTech industry having full access to the teaching, learning and assessment of “at risk” students. The “funders” and “supporters” of the Alliance for Excellent Education and Stanford Center for Opportunity Policy in Education are a “who’s who” of education reform venture philanthropists and industry trade associations. They are those who stand to profit from EdTech’s full takeover of schools, particularly in the most subordinated communities. The lead author of the report and founder of the Stanford Center for Opportunity Policy in Education is Linda Darling Hammond, a prominent education policy leader who is at once known to a be an advocate of teachers, while also being an active proponent of education reform policies, including Common Core State Standards. She was also a developer of one of its aligned tests – Smarter Balanced. Alfie Kohn goes on to point out:

Two corresponding groups of educators seem particularly enamored with EdTech, “those who are awed by anything that emanates from the private sector, including books about leadership whose examples are drawn from Fortune 500 companies and filled with declarations about the need to “leverage strategic cultures for transformational disruption”; and those who experience excitement that borders on sexual arousal from anything involving technology—even though much of what falls under the heading “ed tech” is, to put it charitably, of scant educational value.

Recent and more rigorous international studies report that reading comprehension and assessment performance is encumbered when student learners use digital text (via computers, tablets and smartphones) compared to paper text. Many of these studies also report that subjects have a preference for readings text on paper.

According to a 2015 global study sponsored by the Organisation for Economic Co-operation and Development (OECD), in countries where students commonly use EdTech for schoolwork, students’ reading performance declined. In countries that invest heavily in EdTech for education, the results concluded there is no noticeable improvement in student achievement in either math or science. The study, which took into account social background and student demographics, concluded that technology does not close the “achievement gap” between privileged and impoverished students. The findings also report that students who spend significant amount of time online are prone to feelings of loneliness.

In 2016, researchers from Carnegie Mellon University and Dartmouth College found that reading on computers, tablets and smartphones significantly reduces reading comprehension, and causes people “to ‘retreat’ to the less cognitively-demanding lower end of the concrete-abstract continuum.” Or as James Titcomb describes it in The Telegraph, this technology makes “people unable to fully understand what they are reading as our brains retreat into focusing on small details rather than meanings.”

In another 2016 study, whose subjects were high performing cadets at WestPoint, researchers at the Massachusetts Institute of Technology concluded that the use of electronic devices in classrooms “have a substantial negative effect on academic performance.” A 2015 study by the Georgia Institute of Technology found that “participants who read text on paper tended to take more notes and spend more time studying than those who read from a screen.”

While there is mounting evidence that prioritizing EdTech in the classroom thwarts educational outcomes that are based in curiosity, inquiry, creativity and critical thinking; there is also growing evidence that it can also damage the mental and psychological well-being of children.

A 2015 study titled “Growing Up Digital (GUD) Alberta” was conducted by researchers from the Alberta Teachers’ Association, the University of Alberta, Boston Children’s Hospital, and Harvard Medical School. The purpose of the study was to gain a better understanding of the scope of physical, mental and social consequences of digital technologies on child development, specifically in the realms of exercise, homework, identity formation, distraction, cognition, learning, nutrition, and sleep quality and quantity. Researchers conducted a stratified random sample of 3,600 teachers and principals across Alberta Canada, resulting in over 2, 200 participants that generated a highly representative sample of Alberta’s teaching population, which corresponds with the profession’s demographics. The findings of the study are alarming. Correlating with the increased use of digital technology in Alberta schools, respondents reported that student learning has been in steady decline. According to the study’s authors:

There is a strong sense among a majority of teaching professionals within this sample that over the past 3-5 years students across all grades are increasingly having a more difficult time focusing on educational tasks (76%), are coming to school tired (66%), and are less able to bounce back from adversity (ie lacking resilience) (62%). Concurrent to this, 44% of teachers note a decrease in student empathy, and over half of the sample (56%), reported an increase in the number of students who have discussed with them incidents of online harassment and/or cyberbullying. When asked how the number of students with “diagnosed” health issues has changed in their classrooms, the following three conditions were reported by a majority of teachers to have increased: anxiety disorders (85%), Attention Deficit Disorder and Attention Deficit Hyperactive Disorder (75%), and mood disorders such as depression (73%).

In his 2016 Time magazine article titled “Screens In Schools Are a $60 Billion Hoax,” neuropsychologist Nicholas Kardaras anecdotally offers, “I’ve worked with over a thousand teens in the past 15 years and have observed that students who have been raised on a high-tech diet not only appear to struggle more with attention and focus, but also seem to suffer from an adolescent malaise that appears to be a direct byproduct of their digital immersion.” Kardaras goes on to point to hundreds of peer-reviewed studies that link children’s “screen time” with increased rates of depression, anxiety, ADHD, addiction, aggression and in some cases psychosis.

Risk and the Disciplining of Resistance

Since many dimensions of the state-finance partnership reaches deeply into the daily lives of billions of people across the globe in novel ways; daily patterns, choices and potential for resistance are apparent to – and an extension of – the global financial market. Sites and sources of resistance and compliance to this social order is conveyed through the Big Data surveillance infrastructure and in themselves are becoming sources of financial speculation and derivative (bets) markets. As an intangible sphere of accumulation designed to facilitate both competition and cooperation between professional investors, it is virtually ungovernable. Even if state actors were motivated to stop, or effectively regulate this machine, they cannot for a number of reasons. First, the machine is structurally entwined with the interests of the most powerful and violent nation-states, particularly the U.S. Second, any meaningful disruptions of the machine will crash the global economy. The state’s role to both deceptively and openly collude with finance were at play after the 2008 “liquidity crisis.” The U.S. federal government started by deregulating finance and thereafter protected risky financial activity, then bailed out the largest investment banks and devised the parchment barrier that is the Dodd-Frank Wall Street Reform and Consumer Protection Act.

At the behest of the comprehensive and unaccountable web of power that is the state-finance matrix, the role of the state is more authoritarian than ever. Domestically, militarized austerity and sophisticated surveillance and security apparatuses are at its disposal and are part of everyday life for most Americans; and even more so when resistance is deemed too disruptive to financial markets. As part of this, within the last several decades, finance capital and neoliberal states have learned from and adapted to dissent and resistance. Once tried and true tactics and strategies in the pursuit of state protections can now be effectively ignored, dismissed, tolerated, coopted and preempted. This reality, along with the diffuse power of global finance and its proxy authoritarian states render the pursuit of basic human needs and rights ineffective. Combined, these dynamics have extensively neutralized how resistance movements have historically leveraged power. Instead, as the critical scholar Max Haiven describes it, the social order of neoliberal financialization and its advanced surveillance infrastructure, predicts and integrates resistance into its risk speculations, “factored into financial flows in advance as ‘risk’: the present calculus of future probabilities.” Haiven goes on to explain:

With this hyper-commodification of risk, finance has become a vast, interconnected, pulsating organ fed by billions of local readings of “liquidity” and “resistance” which are constantly coursing through the system, being decomposed and rebundled in patterns… [and] the final result is this: finance as we now have it, as a system that “reads” the world by calculating the “risk” of “resistance” to “liquidity” and allocating resources accordingly, already incorporates “resistance” into its “systemic imagination.”

From UAW members in Ford plants resisting pension cuts, indigenous revolutionary movements in Bolivia and Venezuela, Black Lives Matter in the U.S., to the groundswell of support for a U.S. presidential candidate campaigning as a democratic socialist; finance capital “imagines” these (and many other) possibilities and their disruptive potentials so as to incorporate associated risks, as Haiven puts it, “into its internal equilibrium.” These calculations can then determine preemptive or subsequent interventions and disciplining actions. Thus, financial speculation is a means of “reading” and “indexing” resistance. Finance is also preventing future resistance through the application of economic performativity, which explains the ways that financial instruments can calculate and construct financial actualities that will shape and ensure the futures on which investors speculate.

As part of this, based on the logic of derivative speculation, “risk management” creates a paradigm of neoliberal biopolitics that sorts groups of people according to an economic pyramid that demarcates their market – and therefore their social – value. Those who are doing the sorting are the exalted risk-takers who “hedge” their subject position into wealth, power and prestige. In varied degrees everyone else is viewed as flexible workers and debt instruments to be exploited for the purposes of securitization, speculation and predictable cash flows. Those at the bottom of the pyramid are assigned to perpetual austerity and criminalization; their value is derived from being subjugated and rigidly controlled sources of predictable cash flows via government funds – schooling, prisons, impact investing, subsidies, bond markets, etc.

State-finance authoritarianism and repression through militarized austerity along with far-reaching surveillance and security apparatuses work in tandem with other forms of disciplining. School choice, charter schools, policed schools, standardized curriculum and punitive tests that sort students, determine funding as well as the fate of schools and teachers are forms of disciplining attached to the financialization of education. Finance also disciplines political, economic and social actors more directly. For example, if federal and state governments in the U.S. are compelled to reverse existing policies that serve neoliberal financialization and instead reinstitute Keynesian policies, or dare to move in a more emancipatory direction; financial markets would quickly interpret and respond to these moves by devaluing the U.S. dollar and bond prices while divesting from equity shares in “risky” ventures. This type of financial disciplining can easily lead to larger destabilization within the “house of cards” that is the financialized economy. While its existence is destructive, its disruption can also have catastrophic effects. Therefore, forms of viable “resistance” do not even need to be successful for the state and markets to preemptively intervene and discipline. The mechanisms for disciplining and maintaining social order are also ready-made and built into the founding structures of the U.S. cultural political economy. The hegemony of market ideology is often enough. If not the Constitution’s electoral college, the stacked federalist system of government, the corporate two-party system and its delegate scheme as well as the ability of capital to influence or direct social, cultural and political affairs also effectively mollifies substantive resistance. Additionally, as Maiven describes,

…firms are increasingly pressured to increase exploitation and surveillance of workers, and attack union and workers rights, in order to improve their credit rating and share price. And local, regional and national governments are, in an age of austerity, compelled to destroy public power (invested in public space, welfare programs, civil services, public employment, and collective projects) in response to financial pressures and massive deficits (caused, in effect, by decades of corporate tax cuts and the massive transfer of public wealth into private hands).

Financial disciplining also applies to the daily life of families and individuals, where forms and levels of resistance to finance capital is moderated by employment, income, housing, transportation and food insecurity; individual debt; education expenditures; concerns about healthcare; and saving for elderly years. Fears of disrupting any sites where these needs and concerns exist have an understandable chilling effect.

In summary, this all encompassing Big Data surveillance infrastructure simultaneously reveals and harbors the myth of American democracy, is the engine by which finance capitalism further commodifies our lives and undermines our labor power; intensifies the violence of white supremacy, social inequity, and economic inequality. These dynamics, combined with the nation’s underlying culture of domination, provide fertile ground for the hypermilitarized and authoritarian society that the United States promises to become in the coming decades, if not already.

Tim Scott is an educator, critical theorist and social worker who has been a community and union activist and organizer for nearly 20 years. This work has involved holding union leadership and staff positions (lead organizer and field rep); anti-racism work; global justice organizing (“anti-globalization movement”) within international networks resisting IMF/World Bank/free-trade/structural adjustment policies; harm reduction advocacy within LGBTQ networks; resisting US/UN Iraq Sanctions; Single-Payer Health Care organizing; anti-war/Counter Military Recruitment work; grass-roots media (radio & publication) and being a public education activist. Read other articles by Tim.